{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "9b2cba1c",
   "metadata": {},
   "source": [
    "# Imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "4b0b1683",
   "metadata": {},
   "outputs": [],
   "source": [
    "from matplotlib import pyplot as plt\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import os\n",
    "import numpy as np\n",
    "\n",
    "import json\n",
    "\n",
    "from transformers import MarianTokenizer\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import collections\n",
    "from collections import defaultdict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "ec6f0b86",
   "metadata": {},
   "outputs": [],
   "source": [
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "5b1607e1",
   "metadata": {},
   "outputs": [],
   "source": [
    "src_lang = \"en\"\n",
    "translator = \"opus-mt\""
   ]
  },
  {
   "cell_type": "markdown",
   "id": "adf37372",
   "metadata": {},
   "source": [
    "# Defining plot styles"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "603516f6",
   "metadata": {},
   "outputs": [],
   "source": [
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "from scipy.stats import pearsonr, kde, gaussian_kde\n",
    "import matplotlib\n",
    "from matplotlib import rc\n",
    "\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "6c6d06be",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "\n",
    "plt.rcParams['font.family'] = 'serif'\n",
    "plt.rcParams['text.usetex'] = True\n",
    "# plt.rcParams['text.usetex'] = False\n",
    "plt.rcParams['font.family'] = 'serif'\n",
    "plt.rcParams['font.serif'] = ['Times New Roman']\n",
    "\n",
    "# plt.rcParams['font.monospace'] = 'Ubuntu Mono'\n",
    "plt.rcParams['font.size'] = 26\n",
    "plt.rcParams['axes.labelsize'] = 26\n",
    "plt.rcParams[\"axes.labelweight\"] = \"bold\"\n",
    "plt.rcParams['axes.titlesize'] = 26\n",
    "plt.rcParams['axes.titleweight'] = 'bold'\n",
    "plt.rcParams['xtick.labelsize'] = 16\n",
    "plt.rcParams['ytick.labelsize'] = 16\n",
    "plt.rcParams['legend.fontsize'] = 18\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dc0404cb",
   "metadata": {},
   "source": [
    "# Load / Process Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "0750868d",
   "metadata": {},
   "outputs": [],
   "source": [
    "RECALL=-3\n",
    "PRECISION=-1\n",
    "\n",
    "TRANSLATOR=\"opus-mt\"\n",
    "SRC_LANG=\"en\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "c1aafcb8",
   "metadata": {},
   "outputs": [],
   "source": [
    "def load_old_tokens_dict(tokens_file):\n",
    "    with open(tokens_file, \"r\") as f:\n",
    "        tokens_str = (f.readlines())[0]\n",
    "    tokens_str = tokens_str.replace(\"'\", \"\\\"\")\n",
    "    tokens_dict = json.loads(tokens_str)\n",
    "    \n",
    "    if 'worker' in tokens_dict:\n",
    "        tokens_dict.pop('worker')\n",
    "        \n",
    "    return tokens_dict\n",
    "\n",
    "\n",
    "def create_tokens_dict(variants_file, translator, src_lang, tgt_lang):\n",
    "    tokens_dict = defaultdict(dict)\n",
    "    tokenizer = MarianTokenizer.from_pretrained(f\"Helsinki-NLP/{translator}-{src_lang}-{tgt_lang}\")\n",
    "    \n",
    "    with open(variants_file, 'r') as var_json:\n",
    "        variants_dict = json.load(var_json)\n",
    "    \n",
    "    for prof_gender, variants in variants_dict.items():\n",
    "        prof, gender = prof_gender.split('-')\n",
    "        gender = gender.capitalize()\n",
    "        for v_idx, var in enumerate(variants):\n",
    "            with tokenizer.as_target_tokenizer():\n",
    "                tokenized = tokenizer(var)\n",
    "                num_tokens = len(tokenized['input_ids']) - 1\n",
    "            \n",
    "            tokens_dict[f\"{prof}-{str(v_idx)}\"][gender] = num_tokens\n",
    "    return tokens_dict\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "d538b214",
   "metadata": {},
   "outputs": [],
   "source": [
    "tokens_dict_he = create_tokens_dict(\"../data/wino_mt/he_variants.json\",TRANSLATOR, SRC_LANG, \"he\")\n",
    "tokens_dict_de = create_tokens_dict(\"../data/wino_mt/de_variants.json\",TRANSLATOR, SRC_LANG, \"de\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "987edb87",
   "metadata": {},
   "outputs": [],
   "source": [
    "tokens_dict_es = create_tokens_dict(\"../data/wino_mt/es_variants.json\",TRANSLATOR, SRC_LANG, \"es\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "441cd88c",
   "metadata": {},
   "outputs": [],
   "source": [
    "with open(\"../data/male_stereotype\",\"r\") as f:\n",
    "    male_stereotype = f.readlines()\n",
    "    male_stereotype = {i.strip().lower() for i in male_stereotype}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "789ffaf4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'analyst',\n",
       " 'carpenter',\n",
       " 'ceo',\n",
       " 'chief',\n",
       " 'construction worker',\n",
       " 'cook',\n",
       " 'developer',\n",
       " 'driver',\n",
       " 'farmer',\n",
       " 'guard',\n",
       " 'janitor',\n",
       " 'laborer',\n",
       " 'lawyer',\n",
       " 'manager',\n",
       " 'mechanic',\n",
       " 'mover',\n",
       " 'physician',\n",
       " 'salesperson',\n",
       " 'sheriff',\n",
       " 'supervisor'}"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "male_stereotype"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0bf6d406",
   "metadata": {},
   "source": [
    "# Create Plots"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "bb89a3fc",
   "metadata": {},
   "outputs": [],
   "source": [
    "def graph_5_delta_g(results_file,tokens_dict,lang):\n",
    "    with open(results_file, \"r\") as f:\n",
    "        lines = f.readlines()\n",
    "        recalls_str = lines[RECALL]\n",
    "        recalls_str = recalls_str.replace(\"'\", \"\\\"\")\n",
    "        recalls_dict = json.loads(recalls_str)\n",
    "\n",
    "        precisions_str = lines[PRECISION]\n",
    "        precisions_str = precisions_str.replace(\"'\", \"\\\"\")\n",
    "        precisions_dict = json.loads(precisions_str)\n",
    "\n",
    "    professions = list(tokens_dict.keys())\n",
    "    delta_t_dict = dict()\n",
    "\n",
    "    \n",
    "    for p in professions:\n",
    "        delta_t_dict[p] = tokens_dict[p]['Male'] - tokens_dict[p]['Female']\n",
    "    delta_t_dict = dict(sorted(delta_t_dict.items(), key=lambda item: item[1]))\n",
    "\n",
    "\n",
    "    x = []\n",
    "    y = []\n",
    "    \n",
    "    data = pd.DataFrame({\"$\\Delta T$\": [], \"$\\Delta G$\": [], \"M F1\": [], \"F F1\": [], \"M T\": [], \"F T\": []})\n",
    "    for prof, delta_t in delta_t_dict.items():\n",
    "        prof = prof.lower()\n",
    "        if prof in recalls_dict and prof in precisions_dict:\n",
    "            r_m,r_f=recalls_dict[prof]['male_recall'],recalls_dict[prof]['female_recall']\n",
    "            p_m,p_f=precisions_dict[prof]['male_precision'],precisions_dict[prof]['female_precision']\n",
    "            if r_m ==0 and p_m == 0:\n",
    "                male_f1 = 0\n",
    "            else:\n",
    "                male_f1= 2 * (r_m*p_m)/(r_m+p_m)\n",
    "    \n",
    "            if r_f ==0 and p_f == 0:\n",
    "                female_f1 = 0\n",
    "            else:\n",
    "                female_f1= 2 * (r_f*p_f)/(r_f+p_f)\n",
    "                \n",
    "            data = data.append({\"$\\Delta T$\": delta_t, \"$\\Delta G$\": male_f1 - female_f1,\n",
    "                                \"M F1\": male_f1, \"F F1\": female_f1,\n",
    "                                \"M T\": tokens_dict[prof]['Male'], \"F T\": tokens_dict[prof]['Female']},\n",
    "                               ignore_index=True)\n",
    "\n",
    "\n",
    "    f, ax = plt.subplots(figsize=(7, 6))\n",
    "\n",
    "    sns.boxplot(\n",
    "        data=data, x=\"$\\Delta T$\", y=\"$\\Delta G$\",\n",
    "        orient=\"v\",\n",
    "        notch=False, showcaps=False, showmeans=False,\n",
    "        boxprops={\"facecolor\": (.4, .6, .8, .5)},\n",
    "        medianprops={\"color\": \"coral\"})\n",
    "    sns.stripplot(x=\"$\\Delta T$\", y=\"$\\Delta G$\", data=data,\n",
    "              size=4, color=\".3\", linewidth=0)\n",
    "    \n",
    "#     z = np.polyfit(data['Delta T'], data['Delta G'], 1)\n",
    "#     p = np.poly1d(z)\n",
    "#     ax.plot(data['Delta T'] + len(data['Delta T'].unique())- 1, p(data['Delta T']), \"r--\")\n",
    "\n",
    "    # Tweak the visual presentation\n",
    "    ax.yaxis.grid(True)\n",
    "    sns.despine(trim=False)\n",
    "    \n",
    "    plt.tight_layout()\n",
    "    plt.savefig(f'../graphs/graph_5_{21}_delta_g.pdf')\n",
    "    plt.show()\n",
    "    \n",
    "    print(\"correlation\")\n",
    "    print(data.corr())\n",
    "    \n",
    "    tidy = data.melt(value_vars=['M F1','F F1'], id_vars=['M T'], value_name='F1',var_name='gender')\n",
    "    print(tidy)\n",
    "    \n",
    "    sns.catplot(x=\"M T\", y='F1',\n",
    "        data=tidy, kind=\"bar\", hue='gender', \n",
    "        palette=sns.color_palette(['lightblue', 'brown']),\n",
    "        height=4.5, aspect=1.8)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "4fdcd6c0",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 700x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "correlation\n",
      "            $\\Delta T$  $\\Delta G$      M F1      F F1       M T       F T\n",
      "$\\Delta T$    1.000000   -0.249129 -0.244322  0.161575  0.307190 -0.554959\n",
      "$\\Delta G$   -0.249129    1.000000  0.773641 -0.830557 -0.315953 -0.071033\n",
      "M F1         -0.244322    0.773641  1.000000 -0.289667 -0.500019 -0.235892\n",
      "F F1          0.161575   -0.830557 -0.289667  1.000000  0.037767 -0.100042\n",
      "M T           0.307190   -0.315953 -0.500019  0.037767  1.000000  0.621176\n",
      "F T          -0.554959   -0.071033 -0.235892 -0.100042  0.621176  1.000000\n",
      "    M T gender        F1\n",
      "0   1.0   M F1  0.850000\n",
      "1   1.0   M F1  0.761905\n",
      "2   1.0   M F1  0.776471\n",
      "3   1.0   M F1  0.678261\n",
      "4   1.0   M F1  0.933333\n",
      "..  ...    ...       ...\n",
      "91  1.0   F F1  1.000000\n",
      "92  2.0   F F1  0.800000\n",
      "93  2.0   F F1  0.440000\n",
      "94  2.0   F F1  0.000000\n",
      "95  2.0   F F1  0.750000\n",
      "\n",
      "[96 rows x 3 columns]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 955.109x450 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "graph_5_delta_g(\"../data/de_results.txt\", tokens_dict_de,\"German\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "9b3f1d15",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 700x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "correlation\n",
      "            $\\Delta T$  $\\Delta G$      M F1      F F1       M T       F T\n",
      "$\\Delta T$    1.000000   -0.109746  0.213144  0.229803  0.060523 -0.721798\n",
      "$\\Delta G$   -0.109746    1.000000  0.266439 -0.835765  0.077051  0.137245\n",
      "M F1          0.213144    0.266439  1.000000  0.306559 -0.074438 -0.214405\n",
      "F F1          0.229803   -0.835765  0.306559  1.000000 -0.118498 -0.257679\n",
      "M T           0.060523    0.077051 -0.074438 -0.118498  1.000000  0.647150\n",
      "F T          -0.721798    0.137245 -0.214405 -0.257679  0.647150  1.000000\n",
      "    M T gender        F1\n",
      "0   1.0   M F1  0.714286\n",
      "1   1.0   M F1  0.693333\n",
      "2   1.0   M F1  0.521739\n",
      "3   1.0   M F1  0.794118\n",
      "4   1.0   M F1  0.695652\n",
      "..  ...    ...       ...\n",
      "75  1.0   F F1  0.714286\n",
      "76  2.0   F F1  0.000000\n",
      "77  2.0   F F1  0.230769\n",
      "78  1.0   F F1  0.545455\n",
      "79  2.0   F F1  0.000000\n",
      "\n",
      "[80 rows x 3 columns]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 955.109x450 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "graph_5_delta_g(\"../data/he_results.txt\", tokens_dict_he,\"Hebrew\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "303baf1f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 700x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "correlation\n",
      "            $\\Delta T$  $\\Delta G$      M F1      F F1       M T       F T\n",
      "$\\Delta T$    1.000000   -0.211679  0.064710  0.291136  0.297607 -0.547713\n",
      "$\\Delta G$   -0.211679    1.000000  0.515530 -0.798832 -0.332977 -0.120665\n",
      "M F1          0.064710    0.515530  1.000000  0.103634 -0.258520 -0.278881\n",
      "F F1          0.291136   -0.798832  0.103634  1.000000  0.205014 -0.055722\n",
      "M T           0.297607   -0.332977 -0.258520  0.205014  1.000000  0.635753\n",
      "F T          -0.547713   -0.120665 -0.278881 -0.055722  0.635753  1.000000\n",
      "    M T gender        F1\n",
      "0   1.0   M F1  0.717949\n",
      "1   1.0   M F1  0.666667\n",
      "2   1.0   M F1  0.869565\n",
      "3   1.0   M F1  0.727273\n",
      "4   1.0   M F1  0.769231\n",
      "..  ...    ...       ...\n",
      "71  1.0   F F1  0.816327\n",
      "72  1.0   F F1  0.313725\n",
      "73  1.0   F F1  1.000000\n",
      "74  3.0   F F1  1.000000\n",
      "75  2.0   F F1  1.000000\n",
      "\n",
      "[76 rows x 3 columns]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 955.109x450 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "graph_5_delta_g(\"../data/es_results.txt\", tokens_dict_es,\"Spanish\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "598d6003",
   "metadata": {},
   "outputs": [],
   "source": [
    "def graph_5_delta_s(results_file,tokens_dict,lang, male_stereotype):\n",
    "    with open(results_file, \"r\") as f:\n",
    "        lines = f.readlines()\n",
    "        recalls_str = lines[RECALL]\n",
    "        recalls_str = recalls_str.replace(\"'\", \"\\\"\")\n",
    "        recalls_dict = json.loads(recalls_str)\n",
    "\n",
    "        precisions_str = lines[PRECISION]\n",
    "        precisions_str = precisions_str.replace(\"'\", \"\\\"\")\n",
    "        precisions_dict = json.loads(precisions_str)\n",
    "\n",
    "    professions = list(tokens_dict.keys())\n",
    "    delta_t_dict = dict()\n",
    "    \n",
    "    for p in professions:\n",
    "        if p.split('-')[0] in male_stereotype:\n",
    "            delta_t_dict[p] = tokens_dict[p]['Male'] - tokens_dict[p]['Female']\n",
    "        else:\n",
    "            delta_t_dict[p] = tokens_dict[p]['Female'] - tokens_dict[p]['Male']\n",
    "    delta_t_dict = dict(sorted(delta_t_dict.items(), key=lambda item: item[1]))\n",
    "\n",
    "\n",
    "\n",
    "    data = pd.DataFrame({\"$\\Delta T$\": [], \"$\\Delta S$\": [], \"P F1\": [], \"A F1\": [], \"P T\": [], \"A T\": []})\n",
    "\n",
    "    for prof, delta_t in delta_t_dict.items():\n",
    "        prof = prof.lower()\n",
    "        if prof in recalls_dict and prof in precisions_dict:\n",
    "            r_m,r_f=recalls_dict[prof]['male_recall'],recalls_dict[prof]['female_recall']\n",
    "            p_m,p_f=precisions_dict[prof]['male_precision'],precisions_dict[prof]['female_precision']\n",
    "            if r_m ==0 and p_m == 0:\n",
    "                male_f1 = 0\n",
    "            else:\n",
    "                male_f1= 2 * (r_m*p_m)/(r_m+p_m)\n",
    "    \n",
    "            if r_f ==0 and p_f == 0:\n",
    "                female_f1 = 0\n",
    "            else:\n",
    "                female_f1= 2 * (r_f*p_f)/(r_f+p_f)\n",
    "            \n",
    "            if prof.split('-')[0] in male_stereotype:\n",
    "                # data = data.append({\"Delta T\": delta_t, \"Delta S\": male_f1 - female_f1}, ignore_index=True)\n",
    "                data = data.append({\"$\\Delta T$\": delta_t, \"$\\Delta S$\": male_f1 - female_f1,\n",
    "                    \"P F1\": male_f1, \"A F1\": female_f1,\n",
    "                    \"P T\": tokens_dict[prof]['Male'], \"A T\": tokens_dict[prof]['Female']},\n",
    "                   ignore_index=True)\n",
    "            else:\n",
    "                # data = data.append({\"Delta T\": delta_t, \"Delta S\": female_f1 - male_f1}, ignore_index=True)\n",
    "                data = data.append({\"$\\Delta T$\": delta_t, \"$\\Delta S$\": female_f1 - male_f1,\n",
    "                    \"P F1\": female_f1, \"A F1\": male_f1,\n",
    "                    \"P T\": tokens_dict[prof]['Female'], \"A T\": tokens_dict[prof]['Male']},\n",
    "                   ignore_index=True)\n",
    "\n",
    "\n",
    "    f, ax = plt.subplots(figsize=(7, 6))\n",
    "\n",
    "    sns.boxplot(\n",
    "        data=data, x=\"$\\Delta T$\", y=\"$\\Delta S$\",\n",
    "        orient=\"v\",\n",
    "        notch=False, showcaps=False,showmeans=False,\n",
    "        boxprops={\"facecolor\": (.6, .8, .4, .5)},\n",
    "        medianprops={\"color\": \"coral\"})\n",
    "    sns.stripplot(x=\"$\\Delta T$\", y=\"$\\Delta S$\", data=data,\n",
    "              size=4, color=\".3\", linewidth=0)\n",
    "    \n",
    "#     z = np.polyfit(data['Delta T'], data['Delta G'], 1)\n",
    "#     p = np.poly1d(z)\n",
    "#     ax.plot(data['Delta T'] + len(data['Delta T'].unique())- 1, p(data['Delta T']), \"r--\")\n",
    "\n",
    "    # Tweak the visual presentation\n",
    "    ax.yaxis.grid(True)\n",
    "    sns.despine(trim=False)\n",
    "    \n",
    "    plt.tight_layout()\n",
    "    plt.savefig(f'../graphs/graph_5_{lang}_delta_s.pdf')\n",
    "    plt.show()\n",
    "    \n",
    "    print(\"correlation\")\n",
    "    print(data.corr())\n",
    "    \n",
    "    tidy = data.melt(value_vars=['P F1','A F1'], id_vars=['P T'], value_name='F1',var_name='stereotpe')\n",
    "    print(tidy)\n",
    "    \n",
    "    sns.catplot(x=\"P T\", y='F1',\n",
    "        data=tidy, kind=\"bar\", hue='stereotpe', \n",
    "        palette=sns.color_palette(['mediumaquamarine', 'darkorange']),\n",
    "        height=4.5, aspect=1.8)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "5d092d29",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 700x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "correlation\n",
      "            $\\Delta T$  $\\Delta S$      P F1      A F1       P T       A T\n",
      "$\\Delta T$    1.000000   -0.414946 -0.268046  0.297326  0.556090 -0.477994\n",
      "$\\Delta S$   -0.414946    1.000000  0.567724 -0.776445 -0.113728  0.322013\n",
      "P F1         -0.268046    0.567724  1.000000  0.077973 -0.234556  0.037766\n",
      "A F1          0.297326   -0.776445  0.077973  1.000000 -0.041826 -0.361062\n",
      "P T           0.556090   -0.113728 -0.234556 -0.041826  1.000000  0.464219\n",
      "A T          -0.477994    0.322013  0.037766 -0.361062  0.464219  1.000000\n",
      "    P T stereotpe        F1\n",
      "0   1.0      P F1  0.693333\n",
      "1   1.0      P F1  0.521739\n",
      "2   1.0      P F1  0.794118\n",
      "3   1.0      P F1  0.866667\n",
      "4   1.0      P F1  0.676923\n",
      "..  ...       ...       ...\n",
      "75  2.0      A F1  0.714286\n",
      "76  2.0      A F1  0.695652\n",
      "77  3.0      A F1  0.571429\n",
      "78  2.0      A F1  0.736842\n",
      "79  3.0      A F1  0.133333\n",
      "\n",
      "[80 rows x 3 columns]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 960.826x450 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "graph_5_delta_s(\"../data/he_results.txt\", tokens_dict_he,\"Hebrew\", male_stereotype)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "3d70fbc7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 700x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "correlation\n",
      "            $\\Delta T$  $\\Delta S$      P F1      A F1       P T       A T\n",
      "$\\Delta T$    1.000000   -0.368684 -0.296008  0.303112  0.622846 -0.540084\n",
      "$\\Delta S$   -0.368684    1.000000  0.798157 -0.826557 -0.172690  0.260378\n",
      "P F1         -0.296008    0.798157  1.000000 -0.320632 -0.301795  0.033546\n",
      "A F1          0.303112   -0.826557 -0.320632  1.000000 -0.010448 -0.378039\n",
      "P T           0.622846   -0.172690 -0.301795 -0.010448  1.000000  0.322041\n",
      "A T          -0.540084    0.260378  0.033546 -0.378039  0.322041  1.000000\n",
      "    P T stereotpe        F1\n",
      "0   1.0      P F1  0.850000\n",
      "1   1.0      P F1  0.761905\n",
      "2   1.0      P F1  0.678261\n",
      "3   1.0      P F1  0.747664\n",
      "4   1.0      P F1  0.727273\n",
      "..  ...       ...       ...\n",
      "91  4.0      A F1  0.590164\n",
      "92  2.0      A F1  0.326531\n",
      "93  4.0      A F1  0.000000\n",
      "94  2.0      A F1  0.689655\n",
      "95  2.0      A F1  0.805556\n",
      "\n",
      "[96 rows x 3 columns]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 960.826x450 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "graph_5_delta_s(\"../data/de_results.txt\", tokens_dict_de,\"German\", male_stereotype)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "78a76a23",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 700x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "correlation\n",
      "            $\\Delta T$  $\\Delta S$      P F1      A F1       P T       A T\n",
      "$\\Delta T$    1.000000   -0.365170 -0.334510  0.172641  0.509667 -0.521736\n",
      "$\\Delta S$   -0.365170    1.000000  0.692809 -0.695265 -0.448032 -0.069183\n",
      "P F1         -0.334510    0.692809  1.000000  0.036622 -0.419159 -0.072045\n",
      "A F1          0.172641   -0.695265  0.036622  1.000000  0.203100  0.024066\n",
      "P T           0.509667   -0.448032 -0.419159  0.203100  1.000000  0.468078\n",
      "A T          -0.521736   -0.069183 -0.072045  0.024066  0.468078  1.000000\n",
      "    P T stereotpe        F1\n",
      "0   1.0      P F1  0.666667\n",
      "1   1.0      P F1  0.869565\n",
      "2   1.0      P F1  0.727273\n",
      "3   1.0      P F1  0.785047\n",
      "4   1.0      P F1  0.756757\n",
      "..  ...       ...       ...\n",
      "71  2.0      A F1  0.683761\n",
      "72  2.0      A F1  0.701754\n",
      "73  2.0      A F1  0.745455\n",
      "74  2.0      A F1  0.685714\n",
      "75  3.0      A F1  0.717949\n",
      "\n",
      "[76 rows x 3 columns]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 960.826x450 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "graph_5_delta_s(\"../data/es_results.txt\", tokens_dict_es,\"Spanish\", male_stereotype)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
